Labor shortage is currently significant challenge in global agriculture, while both land use and labor is affecting agricultural food supply. In this report, we aim to analyze the trend of agricultural land use and labor from 1961 to 2019 in 16 key agricultural countries, investigating the relationship between labor issues in agriculture and land use to understand their interdependencies and effects on global agriculture.
The dataset comprises agricultural land use, labor and food supply from 1961 to 2019 for 16 major agricultural production countries, providing insights into global agricultural trends and practices.The data sets with 944 observations and 6 variables were extracted from Our World in Data [https://ourworldindata.org/grapher/agricultural-labor-land]. It is an open-source database and can be used for research and analysis purposes.
The extracted dataset has variables from countries including Australia, Brazil, Canada, China, France, Germany, India, Mexico, Netherlands, New Zealand, Russia, South Africa, South Korea, Turkey, UK, USA from 1961 to 2019. There are three numerical variables– agricultural land use for the sum of croplands and permanent pastures for livestock grazing, agricultural labor for the number of people in agriculture, which includes hiredlabor and unpaid family labor, and agricultural food supply for the total output of agricultural products.
#Import the dataset.
data <- read.csv("agricultural_labor_land.csv")
data1 <- data.frame(data)
The dataset contains 6 variables and 944 observations. The figure of the code is showing as the following.
# Display the screenshot of code
knitr::include_graphics("Image/Screenshot.png")
# Create a table with variable names for data1
variable_names <- data.frame(Variable = names(data1))
kable(variable_names, caption = "Variable Names of dataset")
| Variable |
|---|
| Entity |
| Code |
| Year |
| ag_land_index |
| labor_index |
| food_supply_per_capita |
str(head(data1, 2))
## 'data.frame': 2 obs. of 6 variables:
## $ Entity : chr "Australia" "Australia"
## $ Code : chr "AUS" "AUS"
## $ Year : int 1961 1962
## $ ag_land_index : num 91.9 93.9
## $ labor_index : num 140 140
## $ food_supply_per_capita: num 1.36 1.67
# Select 2 numerial variables and one character, calculate two summary statistics
summary_stats <- data1 %>%
select(Entity, Year, ag_land_index,labor_index) %>%
group_by(Entity) %>%
summarise(
mean_land_use = mean(ag_land_index, na.rm = TRUE),
variance_land_use = var(ag_land_index, na.rm = TRUE),
mean_labor = mean(labor_index, na.rm = TRUE),
variance_labor = var(labor_index, na.rm = TRUE),
)
kable(head(summary_stats, 10))
| Entity | mean_land_use | variance_land_use | mean_labor | variance_labor |
|---|---|---|---|---|
| Australia | 100.10170 | 13.886922 | 125.54146 | 170.23519 |
| Brazil | 80.48894 | 283.270685 | 132.93399 | 325.02212 |
| Canada | 102.12398 | 13.154989 | 152.13008 | 1870.95271 |
| China | 90.94096 | 46.200156 | 134.58977 | 607.19428 |
| France | 99.47961 | 6.851704 | 231.63034 | 18205.34443 |
| Germany | 102.02014 | 5.943474 | 295.73544 | 31186.82249 |
| India | 87.12817 | 95.128728 | 91.31982 | 268.88501 |
| Mexico | 90.89976 | 110.093270 | 87.12339 | 133.00203 |
| Netherlands | 89.83478 | 48.221104 | 152.92678 | 1853.03063 |
| New Zealand | 147.89999 | 1345.215344 | 102.38662 | 91.59432 |
From this data summary, it can be observed that Germany and France have the highest mean labor values, suggesting a significant workforce in agriculture, with Germany also exhibiting the highest variance in labor, indicating large fluctuations or diversity in agricultural labor over the observed period. Additionally, New Zealand stands out with a notably high mean land use and the greatest variance in land use, which could imply extensive and varied agricultural practices in terms of land utilization.
# Create one figure related with research question.
long_data <- data1 %>%
gather(key = "Type", value = "Value", ag_land_index, labor_index)
aggregated_data <- long_data %>%
group_by(Year, Type) %>%
summarise(Total = sum(Value, na.rm = TRUE))
## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.
ggplot_object <- ggplot(aggregated_data, aes(x = Year, y = Total, color = Type)) +
geom_point() +
scale_x_continuous(breaks = seq(min(aggregated_data$Year), max(aggregated_data$Year), by = 5)) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "bottom") +
labs(title = "Total Land Use vs. Total Labor of Main Agricultural Countries",
x = "Year",
y = "Total Value")
# Convert to an interactive plot
interactive_plot <- ggplotly(ggplot_object)
# Display the plot
interactive_plot
Figure 1: Total Land Use vs. Total Labor of Main Agricultural Countries from 1961 to 2019
# Add additional figure and table
# Agricultural Labor over 16 countries from 1961 to 2019
p <- ggplot(data1, aes(x = Year, y = labor_index, color = Entity)) +
geom_point() +
theme_minimal() +
labs(title = "Scatter Plot for agricultural labor over 16 countries",
x = "Year",
y = "Labor")
# Convert to a plotly interactive object
p_interactive <- ggplotly(p, tooltip = c("x", "color"))
# Show the interactive plot
p_interactive
Figure 2: Agricutural Labor over 16 countries from 1961 to 2019
# Use table to describing the data
# Summarize data by entity
summarized_data <- data1 %>%
group_by(Entity) %>%
summarise(
mean_labor = mean(labor_index, na.rm = TRUE),
mean_land_use = mean(ag_land_index, na.rm = TRUE),
mean_food_supply = mean(food_supply_per_capita, na.rm = TRUE)
)
# Create a table with kable
kable(summarized_data, caption = "Labor, Land Use, and Food Supply by Entity")
| Entity | mean_labor | mean_land_use | mean_food_supply |
|---|---|---|---|
| Australia | 125.54146 | 100.10170 | 3.815671 |
| Brazil | 132.93399 | 80.48894 | 23.065022 |
| Canada | 152.13008 | 102.12398 | 9.242143 |
| China | 134.58977 | 90.94096 | 5.164526 |
| France | 231.63034 | 99.47961 | 8.833132 |
| Germany | 295.73544 | 102.02014 | 6.802649 |
| India | 91.31982 | 87.12817 | 7.276023 |
| Mexico | 87.12339 | 90.89976 | 125.825708 |
| Netherlands | 152.92678 | 89.83478 | 2.775343 |
| New Zealand | 102.38662 | 147.89999 | 2.330976 |
| Russia | 184.48464 | 105.25769 | 0.502203 |
| South Africa | 128.68122 | 101.63561 | 107.369080 |
| South Korea | 240.43667 | 119.06752 | 9.407233 |
| Turkey | 93.69406 | 99.55939 | 14.099609 |
| United Kingdom | 157.05209 | 108.34152 | 3.015138 |
| United States | 122.41994 | 106.93250 | 10.817130 |
The analysis of agricultural data for major agricultural countries reveals significant trends in land use and labor. The data shows a steady increase in land use until 1990, followed by a period of fluctuation and a slight decrease, alongside a consistent decline in labor. This suggests a shift towards more efficient, mechanized farming, driven by technological, economic, and policy changes. The substantial labor decrease, despite varying land use trends, indicates a workforce restructuring, potentially due to urban migration and demographic shifts.
Moreover, country-specific data reveals diverse agricultural dynamics. While Germany, France, and South Korea experience significant labor declines, countries like Russia, UK, and US show gradual decreases. In contrast, labor in China and India initially increased before declining, and Mexico shows a consistent rise. These variations suggest differing agricultural practices and levels of mechanization, reflecting a complex agricultural landscape shaped by a multitude of global and local factors.